Variability in Factors Influencing Pull Request Merge Decisions: A Microscopic Exploration
Bibliographic record
Abstract
Context: The pull-based development model is a widely adopted practice in dis- tributed version control systems, particularly in open-source projects. In this model, con- tributors submit pull requests proposing changes to the codebase, which are then reviewed and potentially merged by project maintainers. Previous studies have extensively investi- gated the influence of different factors in merge outcome, aiming to generalize their impact across multiple projects. \nObjective: This thesis takes a unique approach by examining these factors at the project level, aiming to understand how the influence of each factor varies across projects. \nMethodology: To achieve this, we conducted a large-scale quantitative analysis on 841,399 pull requests from 1,100 GitHub projects. We constructed fixed-effect logistic regression models for each project and explored the correlations be- tween different factors and merge outcomes. \nResults: Our analysis indicates that the influence of factors varies across projects, both in terms of their order and direction. For example, while contributor experience is highly valued in many projects, it was found to be statistically insignificant in others. Likewise, the likelihood of a successful merge increases with the number of commits in some projects, whereas in others, it has the opposite effect. These findings have implications for both researchers and practitioners.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".